VQ And ASR Based Audio Courseware Retrieval

In the E-learning environment, multimedia courseware is most widely used in daily teaching events. Retrieval services on multimedia courseware can definitely help to improve the quality of the learning by providing the students a convenient way to focus on the part where they have interest. And speech signal based retrieval method is a common technology in retrieving information from multimedia files. With well-developed speech recognition techniques, context based speech retrieval can be a good practice in this field and deserves research efforts.This article introduces a multimedia courseware retrieval method in E-learning environment. Since the variety of professional knowledge courseware may contain in its content, and especially those terminologies mentioned in lecture, it's hardly possible to build a well language for the recognizer. So the method introduced here shall directly process the acoustic level signals to avoid the"out-of-vocabulary (OOV)"problem. Besides, the courseware is recorded in the classroom which makes the audio quality comparatively poor, our method should also be an error tolerant one.The main tasks in this article include:1,Analyze and design the vector quantization (VQ) technique in clustering the hidden Markov model (HMM) states. Generate a codebook for pre-processing the speech signal. This step do the time-consuming feature extraction and comparison work off-line so that the retrieval request can be processed with a quick response. Also, the speech signals are converted into a sequence of state numbers to speed up the pattern matching.2,Multilevel retrieval algorithm afterwards introduces a path searching method for keyword spotting as first-level retrieval. In this stage, a roughly matched set of candidates is produced. Later in the second-level retrieval, automatic speech recognition is introduced to verify all the candidates and give them each a score based on the probability value obtained in the decoding process. At last the results are sorted according to their score and output.3,Implement a prototype system based on the above algorithm and use real courseware for experiment. Evaluate the system performance and analyze the feasibility of using it in a real environment.